Embodied AI2026
Autonomously generates, iteratively debugs, and composes robot control programs into a reusable skill library for discovering and refining manipulation skills across simulation and real robots. Uses a code-as-policy continual learning loop—accumulating validated sensorimotor skills instead of model weights—to tackle long-horizon household and benchmark tasks.
AI Dataset2026
Provides 1,800+ hours of synchronized egocentric multi-view recordings with 3D hand reconstructions, wide‑FOV depth, and hierarchical task/subtask annotations for embodied AI and robot learning. Includes six fisheye views, hand meshes, and per-episode temporal labels across 44k+ episodes.
AI Dataset2026
Generates humanoid robot motion references that preserve object contact locations/timing by solving windowed trajectory optimizations against contact targets in the object frame. Releases retargeted trajectories for two Unitree robots across 75 objects (≈13.9k robot–motion pairs); CC BY‑NC‑SA 4.0.
AI Dataset2026
Provides imagined interaction segments generated by world models for RoboTwin2.0 tasks, stored as fixed-length HDF5 chunks (21 observation frames, 20 actions, rewards and episode flags). Useful for training and evaluating world-model-based policies; currently limited to the RoboTwin2.0 subdataset.
Xingyu Miao, Zizun Li +46Shanghai AI Laboratory: Affiliation: Project page:https://internrobotics.github.io/InternW0-Delta/
Learns joint predictive visual dynamics and action generation for generalist robot manipulation, translating future-relevant visual representations into actions. Integrates a Mixture-of-Transformers coupling a video expert and action expert, a frozen vision-language model for semantics, 4D distillation, and Causal Imprint; pretrained on a 20K+ hour heterogeneous corpus.
AI Agent Papers2026
Records structural priors with skill-specific policies so a runtime agent can select and compose the version of each skill best suited to new states, improving out-of-distribution and compositional generalization for robot manipulation from few demonstrations.
AI Dataset2026
Multimodal real-robot dataset of 28,993 teleoperated bimanual manipulation demonstrations (~300 hours) with synchronized multi-view RGB/tactile videos, 65D robot state/action, 60D fingertip tactile signals, and temporally aligned language segments; intended for imitation, visuomotor and visual‑tactile research, CC BY‑NC 4.0 (non‑commercial).
Embodied AI2026
Lets general-purpose vision-language models directly command robots via a compact mid-level action interface and asynchronous monitoring, enabling zero-shot manipulation without task-specific policy training; demonstrates strong sim benchmarks and real xArm6 transfer.
Embodied AI2026
Evaluates GPT-6-Astra as an embodied policy across 12 robotic benchmarks, examining in‑context learning, hybrid System‑2/System‑1 control, and real‑world deployment gaps; finds strong benchmark competence but practical limits from demonstration cost, latency, and failure recovery.
AI Agent Papers2026
Turns a VLM into a robot-coding agent that directly drives a dual-arm robot from three RGB cameras, iteratively writing and fixing small executable perception and control tools from observations and logs; evaluated on 42 RoboDojo tasks.
Chao Yu, Shu'ang Yu +40Infinigence AI: Affiliation: For the complete list of authors, please refer to the Appendix A Author List and Contributions section
Shares execution traces across deployed robot fleets to improve a single manipulation policy using failure-guided experience retrieval fine-tuning (FERT) and a fault-tolerant asynchronous training system (FATS). Demonstrates large gains in real-world tasks and higher success with larger fleets, without extra expert corrective actions.